Smart garment and method for detection of body kinematics and physical state

ABSTRACT

A body garment including sensors distributed throughout the garment, each sensor senses body state information from a local surface area of a body; and sensor nodes in proximity to the plurality of sensors, each sensor node including a processor to receive sensing body state information from at least one of the plurality of sensors. Each processor is configured to receive body state information locally from sensors, to utilize the information to determine a local surface shape of the surface of a portion of the body part; and to exchange local surface shape information with neighboring sensor nodes. At least one processor of utilizes the local surface shape information received from the sensor nodes to generate one overall model of a surface shape of the entire surface of the body part covered by the garment.

FIELD OF THE INVENTION

The present invention relates to sensors and electronic circuits, andmore specifically to a smart garment/suit for detection of bodykinematics and physical state and a method thereof.

BACKGROUND

Conventional full body bio-mechanical measurement systems use camerasand/or rigidly affixed sensors, for example on or around a treadmill toget a full body pose picture. However, affixed sensors requirerestrictive equipment to be used and failures occur when sensors moveout of position. Precisely affixed sensors result in poor performance ifnot placed precisely or if their position changes while in use.Similarly, static camera positions pose the problem of not allowing formany behaviors/movements to take place while measurement is occurring,due to a limited field of view or restrictions imposed by treadmill sizeand speed and static direction. None of these methods/systems arecompatible with field maneuvers and during complex athletic maneuvers bya subject while performing in the target environment, for example,during a mission of a soldier.

SUMMARY OF THE INVENTION

In some embodiments, the present invention is directed to a smartgarment and a method thereof for detection of body kinematics andphysical states, which incorporates many sensors affixed to the suit.The invention optimally uses data from these sensors to derive bodystate information, such as pose, motion and muscle activation. Theinvention is capable of collecting and processing data in real time forcontrol of a variety of applications that require pose, activation ormovement as inputs. Examples of such applications include actuatorinterfaces for mechanical assisting, haptic feedback devices fordelivering injury warnings, biomechanical systems designed to analyzemetabolic activity or athletic load endured by the subject, prostheticlimbs and assistance devices, and health monitoring systems.

In some embodiments, the present invention is a body garment fordetecting body kinematics. The body garment includes a plurality ofsensors distributed throughout the garment, each sensor being configuredto sense body state information from a local surface area of a body partcovered by the garment; and a plurality of sensor nodes in proximity tothe plurality of sensors respectively, each sensor node including aprocessor and configured to receive sensing body state information fromat least one of the plurality of sensors; wherein each processor isconfigured to receive body state information locally from one or moresensors in proximity to said each processor; to utilize the informationto determine a local surface shape of the surface of a portion of thebody part covered by said one or more sensors; and to exchange localsurface shape information with neighboring sensor nodes, and wherein atleast one processor of at least one sensor node utilizes the localsurface shape information received from the sensor nodes to generate oneoverall model of a surface shape of the entire surface of the body partcovered by the garment.

In some embodiments, the present invention is a method for detectingbody kinematics. The method includes: collecting body state informationfrom a plurality of local surface areas of a body part covered by agarment, from a plurality of sensors distributed throughout the garmentcovering the body part; locally and in real time processing thecollected body state information of local surface areas to determine alocal surface shape of the surface of a portion of the body part coveredby respective local surface areas, by a respective one of a plurality ofdistributed processors; exchanging local surface shape information withneighboring processors; and generating one overall model of a surfaceshape of the entire surface of the body part covered by the garment fromthe distributed local surface shape information.

In some embodiments, body kinematics information are generated from theoverall model. Furthermore, feedback may be received from thedistributed processors or from external devices, and the feedback andthe body kinematics information are used to adaptively control anactuator.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention, and many of theattendant features and aspects thereof, will become more readilyapparent as the invention becomes better understood by reference to thefollowing detailed description when considered in conjunction with theaccompanying drawings in which like reference symbols indicate likecomponents, wherein:

FIG. 1A shows a conventional hardware configuration for detection ofbody pose and muscle activation.

FIG. 1B shows an exemplary garment with hardware configuration fordetection of body pose and muscle activation, according to someembodiments of the present invention.

FIG. 1C depicts a simplified exemplary architectural block diagram of asystem, according to some embodiments of the present invention.

FIG. 2 illustrates a simplified exemplary hardware block diagram of aportion of the garment, according to some embodiments of the presentinvention.

FIG. 3 is a simplified exemplary block diagram of a sensor node,according to some embodiments of the present invention.

FIG. 4 shows a simplified exemplary block diagram for signal processingand actuator control, according to some embodiments of the presentinvention.

FIG. 5 shows a simplified exemplary block diagram for a controlfunction, according to some embodiments of the present invention.

FIG. 6 depicts a simplified process flow for detecting body kinematics,according to some embodiments of the present invention.

DETAILED DESCRIPTION

The present invention is directed to a smart garment/suit for detectionof body kinematics and physical state, such as body pose and muscleactivation. The invention enables sensor data to be taken anywhere, atany time during any activity of interest. In some embodiments, thepresent invention comprises of a suit with sensors distributedthroughout and especially in places that observe body state information,such as displacement during body movement or activity (for example,athletic movement). State information refers to multiple parameters andfeatures of interest. For example, skin surface temperature, electricalnerve impulses, and whether a person has been at rest or is warmed up.Furthermore, this information can be processed over time to estimatemore complex bio-state information such as fatigue and injury.

By using continuous manifold assumptions, data fusion and adaptivealgorithm techniques, the invention is capable of losing some fractionof sensors as well as displacement of the sensors on the body and yetperform its desired functions. In this sense, a manifold refers to a setof similar and related sensors that respond in a well ordereddistribution. For example, multiple strain sensors over a muscle alldeform in a particular pattern when that muscle is contracted. Also, thenotion of being adaptive is not limited to simply tailoring to anindividual/subject, but to also evolve over time. A continuousalgorithm/process is an algorithm/process that operates on data thatrepresents continuous quantities, even though this data is representedby discrete approximations.

In some embodiments, the processing of the sensor data takes place on acentralized processor or a set of distributed processors placedthroughout the garment and working in unison to compute the relevantfeatures of the sensor data and advance the kinematic model tracking thebody movement. In the case of distributed processing in the garment, theinvention is capable of losing some fraction of (distributed) processorsand communication links and yet will continue to provide results equalto or of minor degraded fidelity than if the entire suit was intact.

FIG. 1A shows a conventional hardware configuration for detection ofbody pose and muscle activation. As shown, strain gauges 102 and 104 areplaced at specific locations of a leg 101 to measure muscle activationand joint flexure. For example, a circumferential strain gauge 102 isplaced around the leg to measure expansion of quadriceps muscle.Similarly, a longitudinal strain gauge (or any sensor capable ofmeasuring the angle of the knee) 104 is placed over the knee cap tomeasure the knee angle. This data can then be fed to a computerimplemented process/algorithm to determine the corresponding quadricepsactivation and knee flexure. As described above, these (precisely)affixed strain gauges 102 and 104 result in poor performance if notplaced precisely or if their position changes while in use. Theresulting data are also descriptive only of the particular muscle andjoint angle that are instrumented.

FIG. 1B shows an exemplary garment with hardware configuration fordetection of body pose and muscle activation, according to someembodiments of the present invention. As depicted, a plurality ofsensors 106 forming one or more meshes are placed throughout the leg 101to detect body (in this case, leg 101) pose and muscle activation. Forexample, strain gauge nodes 108 in the upper area of the knee sense andgenerate related data for elongation in circumferential direction withmuscle expansions. Likewise, strain gauge nodes 110 around the kneesense and generate related data for elongation in front and compressionin the back of the knee, during knee flex. Pressure sensors at the footarea, for example, at the side and bottom of the foot, sense thepressure exerted on the foot and generate related data.

In some embodiments, muscle activation voltage potentials are sensed byvoltage sensors at the skin. Similarly, changes in muscle transmittancemay be sensed using optical emitter and receiver (sensor) pair. In someembodiments, circumferential changes in muscle groups are sensed usingcompliant piezoelectric bands (strain sensors). The plurality ofsensors, in combination with the computer implemented process describedbelow, yield data that provide a detailed description of the kinematicjoint flexure in multiple axes and the character of actuation (e.g.,flexing/extending/twisting) of the muscles that effect the joint.

This data may then be used to distinguish high-dimensional goalmanifolds or modes, e.g., the gait of the subject. For example: walking,jogging, running or throwing modes. The data may also be used to analyzethe subject's mechanical repeatability or efficiency of a goal,asymmetrical behavior in completion of a goal or metabolic load; usingunique processing techniques specific to each feature. The data from theplurality of sensors 106 may also be utilized to detect fitness,injury/malfunction, fatigue, changes due to different weight bearingloads and health of both the subject and the garment. This data can alsobe used to control a variety of mechanical or robotics actuators, suchas a mechanical assist for walking/running or the seamless integrationof a prosthetic limb.

FIG. 1C depicts a simplified exemplary architectural block diagram of asystem, according to some embodiments of the present invention. As showndata from the sensors 120 are received and locally processed by theirrespective processors 122 in respective sensor nodes, which arephysically interconnected in mesh topology and receive power via thebusses and/or channels 126. In some embodiments, the entire system ispowered by one or more energy storage/generation components (i.e.,batteries, solar cells, thermoelectric generators, etc.) 128. Thesoftware running on the processors 122 implements aspatially-distributed infrastructure 124, which may be the Proto™spatial computing language, that supports data flow and sharedprocessing activity among the discrete processors. It is within thisframework that the adaptive, data fusion, and other algorithms 130 areexecuted. Depending on the particular implementation, these algorithmsmay include adaptive mode estimation 132, some outputs of which may thensupport adaptive mode estimation 134. In cases where the invention isused in conjunction with actuators, the outputs of adaptive modeestimation may in turn support adaptive actuator control 136. In someembodiments, an interface node processor 138 is implemented, possibly asan extension to the functionality of a sensor node processor, tocommunicate with other (external) systems 140.

In some embodiments, the interface to the actuators and their idealfunction for use during a mode can be defined in software and uploadedto the system so the on board controller can activate and control theactuator in the most effective way possible. The adaptive nature of thesoftware design also allows for real-time optimization of controlalgorithms using feedback taken by the suit. In some embodiments, thesensing portion, for example, the garment, may be decoupled from theactuation portion to allow for different actuator applications.

The use of many sensors distributed throughout the garment allows for arobust, compliant solution for a problem conventionally solved byinflexible, rigidly-affixed sensors.

In some embodiments, some of the sensors may be Danfoss PolyPower™stretch sensors, which are textile compatible capacitive mode sensors.In some embodiments, some of the pressure sensors may be COTS Tekscan™pressure sensors with resistive load cell technology.

FIG. 2 illustrates a simplified exemplary hardware block diagram of aportion of the garment, according to some embodiments of the presentinvention. As shown, a plurality of sensors 206 a-206 f are placed anddistributed throughout the garment and especially in places that observedeformation during body movement or activity. Each sensor 206 a-206 f isinterrogated by a respective sensor node 210 a-210 e. However, eachsensor node 210 a-210 e may correspond to more than one sensor. Thesensors 206 sense muscle expansions and joint activity via modalitiesthat may include but are not limited to myoelectrical, electrical nervesensors, accelerometers, thermal sensors, opticaltransmissivity/reflectance, pressure, and/or mechanical strain, andgenerate related data for its respective sensor node 210. For example,muscular injuries can be detected by increased heat in the region of theinjury, sensing nerve impulses and acceleration can result in thekinematic information, and thermal sensors can also provide a sense offatigue or fatigue potential.

Each sensor node 210 comprises of at least one processor and relatedhardware and interfaces for processing the sensed signal from itsrespective sensor(s) and outputting the processed signal to an interfacenode 204 via way communication channels 208. For example, when thewearer puts on the suit/garment, a series of known callisthenicexercises may take place to calibrate the suit/garment and build a modelof body movement, based on the known callisthenic exercises. Once thisis done, changes in the manifold is processed to fit the sensor input tothe best fit of the body movement model. The holistic body state arethen advanced using input from adaptive filters.

In some embodiments, each sensor node is configured to receive bodystate information locally from one or more sensors in proximity to thesensor node; to utilize the information to determine a local surfaceshape of the surface of a portion of the body part covered by said oneor more sensors; and to exchange local surface shape information withneighboring sensor nodes.

The interface node then sends the processed signal to an external systemvia a communication channel 203. The processed signal includeskinematics and physical state of the subject's body, such as pose andmuscle activation information at each sensor location. Knowing thelocation of each sensor and its correspondence to body (parts) positionand/or muscle provides accurate pose and muscle activation informationfor the portion of the body covered by the garment. For example, if asensor is located on the front of the knee to measure strain oriented ina head to toe direction, the increase in strain would correspond to thebending of the knee. Initial calibration provides a range of motionanalysis to indicate the maximum strain to be experienced in normalmovement, and that initial model is evolved by the adaptive processesrunning on the system. As another example, if a muscle density sensor orhorizontally oriented strain sensor is located on or around thehamstring, the increase in density or strain indicates the muscle isactivated.

In some embodiments, the sensor data are used to determine deformationof the epidural manifold over which the suit is worn, that is, theamount of material stretching between points on the garment below andabove the knee going from standing to crouching, allowing for thederivation of the joint positions and the state of the muscles andmuscle groups of the body. In some embodiments, at least one processorof at least one sensor node utilizes the local surface shape informationreceived from the sensor nodes to generate one overall model of asurface shape of the entire surface of the body part covered by thegarment. One skilled in the art would recognize that not all of theperipheral components shown in FIG. 2 are necessary to implement asensor node, according to the present invention. For example, thefunctions of the memories; the IO functions; and/or voltageregulation/management functions may be combined together or some of themomitted.

The use of many sensors allows for over-determination of the bodyposition and allows for sensor noise and ambiguity to be mitigated Thisleads to a high fidelity model of instantaneous body position and muscleactivation measurements that can be saved or ex-filtrated to off-boardcomponents. The distributed sensors and sensor nodes provide higherfidelity gait information than a typical co-located actuator/sensor canprovide, and enable actuator removal/replacement without having tore-design or re-manufacture the garment.

The over-determination also permits the garment to self-configure and toadapt its configuration as the garment shifts on the wearer over time,obviating the need for precise sensor placement. The garment/suit maycover the full-body or partial-body, and the configurations may beimplemented as a single garment or multiple dynamically integratedgarments, for example, a partial-body garment with multiple componentsmay have disconnected components (e.g., arms and legs but no torso).

In some embodiments, the multiple components of a full body or apartial-body garment may be detachable and attachable to the each otherand thus when attached, forming a larger coverage of the body. In thesecases, the processors (interface node) of a component are electrically(or optically) coupled with and recognized by the processors (interfacenode) of the other components, when the component is attached to theother components to cover a larger portion of the body.

In some embodiments, the plurality of sensors 206 and the sensor nodes210 are configured to be energy efficient. For example, certain sensors(and their respective sensor nodes) located on certain locations of thebody/garment, e.g., on certain muscle locations, may be sensing highlevels of activity and/or processing in high resolution or highfidelity, while other sensors (and their sensor nodes) positioned ondifferent location of the body/garment may be sensing low levels ofactivity and/or low resolution/fidelity, thus conserving energy. Inother words, the distributed sensor nodes may be running fast and densein critical areas and slower in stable regions. For example, when anadaptive filter converges on the goal manifold identified as running,any sensors present on the trunk and/or arms would be operating atreduced data (and energy) rates relative to those on the ankles, knees,and/or soles.

In some embodiments, the computations (in the sensor nodes, and/or acentral processing location) are specified in terms of geometriccomputations and information flow. One advantage of this mode ofspecification is that it can enable automated programming methods, suchas those using the amorphous medium continuous abstraction, toautomatically generate a robust distributed algorithm implementation ofsuch a specification. For instance, a bend angle may be computed byintegration of curvature over the knee region, or fatigue may bemeasured by gathering the mean frequency of trembling across the regionof a leg muscle, or the pose of the body may be computed by flowing thepose information computed in a region (e.g., the knee), to other nearbyregions (e.g., upper leg, lower leg, ankle, hip) where it can be fusedwith the geometry of those regions to create a more abstract model ofleg pose. For example, the knee region model may overlap on its edgeswith the upper leg and lower leg models, which in turn overlap on theirother edges with the ankle and hip models. These overlap regions can beused to align the models, allowing construction of a lower-resolutionmodel of the full leg.

FIG. 3 is a simplified exemplary block diagram of a sensor node,according to some embodiments of the present invention. The signal froma sensor 304 is received by a signal conditioning module 306 whichprovides any gain, offset, and/or analog filtering required prior todigitization by the A/D converter 314. Digitized data are provided tothe microcontroller/processor 301. The sensor node 300 is driven by apower supply. A voltage regulator 302 regulates and controls the voltageto the sensor node 300, enabling various components to be deactivated toreduce energy consumption.

Microcontroller/processor 301 includes RAM memory 322 and flash memory326, which are used to store intermediate data in the sensor node;general purpose I/O (GPIO) 324 and communication ports 312 tocommunicate data within and outside of the microcontroller/processor301. A power manager 320 manages power distribution within themicrocontroller/processor 301 to further reduce energy consumption. Ahardware multiplier 318 provides fast and energy-efficientimplementation of the mathematics required by the sensor processingalgorithm(s). A watchdog timer 316 automatically resets themicrocontroller/processor in case of any faults that cause its programto hang. A transceiver 308 communicates information to/from other sensornodes or external systems, using LVDS (Low Voltage DifferentialSignaling) to reduce energy consumption and enhance signal integrity.

In some embodiments, the process/algorithm, running on the sensor nodes(by a respective processor), is generated automatically from aspecification of collective behavior, such as from geometry andinformation flow. One way of accomplishing this is to associate eachcollective operation with a distributed implementationprocess/algorithm, and to associate each method for composing collectiveoperations with an equivalent method for composing distributedimplementations. For example, this approach may be embodied byspecifying collective operations (such as integration over a surfaceregion of sensors, or flow of information along a shortest path betweentwo regions) in the Proto™ programming language. A program thusspecified is a functional composition of mathematical operations onfields over geometric manifolds, such as the surface of a person's body.Established transformation methods embodied in the “MIT Proto™” compilerand virtual machine are then capable of transforming any Proto™ languageprogram into a distributed algorithm that approximates the collectivespecification, such as would be suitable for execution on a collectionof networked sensor nodes as described above.

FIG. 4 shows a simplified exemplary block diagram for signal processingand actuator control, according to some embodiments of the presentinvention. The plant 408 represents the actual physical system that onedesires to affect. This may represent a localized area (e.g. ankle), acollection of areas (e.g. full leg) or the entire body. In typicalembodiments, the plant includes the actuation mechanisms and the suiteof sensors in the associated body suit. In some embodiments, threeprocessing paths shown in the figure accept reference information r thatdefines what actions are being commanded. This command may be derivedfrom the sensors through a process that estimates what movement oraction is being performed by the person.

For the physical path that includes the real plant 408, a commandshaping module 404 properly synchronizes the actuators to achieve thedesired result, such as providing an effort assist while running orlifting, and issues commands u to the actuators in the plant 408. Amodel 402 that represents the total desired system characteristicsprovides a reference against which to compare (410) the actual system(404 and 408 combined). The differences in comparison 410 between actualand desired responses are used in a biometric learning process 406 thattunes the command shaping to better achieve the desired behavior. Thisbiometric learning provides an extensive ability to tune the systemperformance to each individual and their current condition. For example,it can adapt the timing and distribution of actuator force assists tocompensate for changes in gait that may occur over time.

For example, in some embodiments, hip, knee and ankle actuation may beutilized to provide a mobility assist for a running action. As theperson becomes more fatigued over time, his/her pace may slow, form mayshift and compliance during the impact portion of the gate may increase.Through the many detailed sensors (e.g. muscle activation manifold) andaggregate state estimates (e.g. fatigue level), the system is capable ofidentifying these changes and modifying the timing and force levels ofthe actuators to improve form and increase assist level, increase anklestiffness during impact to reduce injury risk and assign an estimatedfatigue level for the person. Using the distributed sensing andactuation, the system modifies the model of the person to reflect theperson's current kinematic form. Furthermore, these metrics can adaptthe nominal model to have, for example, a 0.6 second gate time insteadof 0.5 seconds. This way, the open loop distributed command shapingapplies an appropriate assist based upon the person's current detailedand aggregate state measurements.

In some embodiment, the invention uses (e.g., multivariable) adaptiveprocessing to tune the shape and distribution to command a system ofactuators and sensors. Several known techniques may be used for adaptivecontrol. For example a Linear Quadratic Gaussian (LQG) controller is anobserver-based control of simple multivariable models that usesdistributed actuation and sensing to perform a task; a Least Mean Square(LMS) adaptation technique may tune model parameters and perform systemidentification; an LMS Filtered-X technique may be used to controlcomplex dynamics with respect to a reference command (“X” notation); andbeamforming techniques may be used to group sets of actuators andsensors to practical basic functions (e.g. “walking configuration”).

In some embodiment, the system/garment of the present invention isadaptive in both its control of external devices and its ability tomeasure high-dimensional goal manifolds. This adaptive system is robustto the shifting and imprecision of sensors on the skin/garment. Theadaptive system also accommodates variations in a single user/wearer'sperformance. The human body has variable range of motion, musclestrength and responsiveness, so the degrees of freedom being measured bythe suit while performing a goal changes via the time of day, thetemperature, and the health of the subject along with other events. Thishigh dimensional dynamic data piece is referred to as the goal manifold.The calibration data taken after the subject initializes the systemprovides a baseline for the model to use, however, the model may beupdated throughout the subject's usage of the system.

The original goal manifold for walking, for instance, is a startingpoint. The difference between this manifold and the measured manifold isutilized to update the stored model with more recent information tocorrespond with the reality. In adapting to the garment's positioningover the epidural manifold, the feature vectors is updated to correspondwith the sensor streams that contain the highest signal to noise ratioin identifying the current goal manifold. The adaptive control forexternal devices allows for the system to respond to feedback given bythe suit itself or other data streams coming from the external devices.If adequate information is available, that feedback is used to optimizethe performance of the external device to maximize its value to thesubject.

This system of distributed sensors and sensor nodes is adaptable tohardware and communication faults, that is, in most cases, loss of anode or communication link between nodes is effectively a coarsening ofthe geometric approximation. Periodic updates maintaining the connectionbetween each node and its neighbors suffice to discover these types offaults. Following discovery of a fault, the surviving devicesreapportion the surface that each represents. Certain faults may causelarger-scale disruption, e.g., the loss of a node in the hip area thatwas serving as a relay for information from the knee to reach theshoulders. In this case, an implementation of that distributed geometricalgorithms that uses self-stabilizing geometric algorithms, such asCRF-Gradient, ensures that if any valid geometric construct existsfollowing the fault (e.g., any path from knees to shoulders), it israpidly identified and used.

In a spatial computing approach, sensors (and their data) are consideredto be acting as part of the same unit whenever they are close to oneanother (a parameter that can be set at the system level depending onthe application) and are moving together, for example, their most recentmotions were within a certain speed, distance and/or degrees direction.For instance, a muscle movement can be detected by multiple sensors, andeach sensor can detect multiple muscle movements, so a good place tostart is aggregating detections information from the sensors into theprocessors. That is, each sensor node may run a computation for eachmuscle movement (for example, circumferential changes in muscle) itdetects, and computations share information with neighboringcomputations only if they are about the same muscle movement.

This can be determined for each neighbor of a sensor by comparing therelative position of the local detection with the sum of the neighbor'scurrent value for its detection and the neighbor's relative position.Each muscle movement can be estimated geometrically by tracking whatsensors are entering and exiting its detection region.

To cluster muscle movements into units, for each muscle movement, aregion of every point within a certain distance is selected. Thisensures that if two muscle movements are within a certain distance ofone another (and therefore close enough to be in the same unit), thereis at least one point where their regions overlap. These regions can beconstructed by measuring the distance to the muscle movement and testingwhether the distance is less than the certain distance, producing anindicator field. A function mapping is performed for each point in spaceto a Boolean, true for points in the region and false for points not inthe region. For those sensors that cannot detect the muscle movementdirectly, distance can be estimated by applying the triangle inequality,for example, the minimum over all neighbors of the sum of a neighbor'scurrent value for the estimate and the range to the neighbor.

Sensors that know the current movement (e.g., speed and bearing)estimates for a muscle movement can supply them to others in the regionby taking the gradient of the distance estimates. This produces a vectorfield indicating which direction the estimates should flow from thesource. Given recent speed and bearing estimates, points where tworegions intersect can be compared to determine whether the two musclemovements are part of the same unit.

Finally, the number of muscle movements in a unit and its aggregatevelocity can be computed with surface integrals over the union ofregions for nodes in the same unit. Accordingly, how a distributedalgorithm might be formulated in terms of geometric computations isdemonstrated. More sophisticated applications, for example,incorporating reliability estimates from sensors or accounting forerratic or large movements in the unit definition, can be implementedwith more sophisticated geometric computations.

FIG. 5 shows a simplified exemplary block diagram for a controlfunction, according to some embodiments of the present invention. Agarment 501 with distributed sensors and processors provides localizedinformation about a portion of the body. This information is processedby a beamformer 502 that serves several functions. For instance, thebeamformer 502 provides a scoring mechanism against basis functions 504that are characteristic to the different capabilities of the garment ora larger set of garments. These basis functions may include processedmetrics, such as, cycle time and higher frequency muscle motion (e.g.impact vibrations) to produce key features that are used in a weightedmultiple evidence comparison scheme, such as, the Dempster-Shafermethod.

In some embodiments, features may also be a manifold comparison using aninner product of measured distribution versus candidate manifold shapes.For example, a heavy lifting motion will have certain muscles allactivated in phase, a high level of detected effort, and slow movementtime. A simple standing action would have a lower level of effort and arunning action would have different phasing of the muscles and adifferent motion speed. In a Dempster-Shafer process, each candidateaction would have a statistical likelihood of a feature value beingassociated with the action. Continuing with the heavy lift example,consider the three candidate features of high muscle activation, phase(synchronization) of muscle activation and action speed. From aprobability perspective, a weighted statistical algorithm could assign aweighted probability of 1 for a slow action speed for heavy lift andstanding actions but only a 0.1 for a running action. Table 1 shows anexample of how such features could be used to score measured informationagainst candidate actions for this simple example. The result thenscores a heavy lift as the most probable action being performed.Additional features would improve discrimination among the actions.

TABLE 1 Example Measured Feature Example Basis Function ScoreDescription Value Heavy Lift Standing Running Muscle High 1 .3 1activation intensity Activation In-phase 1 1 0.3 phase Action Speed slow1 0.9 0.1 Simple Total 3 2.2 1.4

This enables the larger control system to select a particular mode 506(e.g. running) and parameters 508 associated with that mode (e.g. gait).The beamformer 502 also provides information for mode shape adaptation510, or tuning. The mode selector 506 chooses which dynamic modecontroller 512 to employ to optimize the estimated action. Thiscontroller issues distributed actuation commands to notional actuationmechanisms 514, 516 and 518.

FIG. 6 depicts a simplified process flow for detecting body kinematics,according to some embodiments of the present invention. As shown inblock 602, body state information is collected from a plurality of localsurface areas of a body part that is covered by the garment. Theinformation is collected from a plurality of sensors that aredistributed throughout the garment covering the body part. The bodystate information may include parameters and features of interest, forexample, skin surface temperature, electrical nerve impulses, andwhether a person has been at rest or is warmed up. The body stateinformation can be processed over time to estimate more complexbio-state information such as fatigue and injury. The collected bodystate information of local surface areas are then processed locally andin real time (by a plurality of processors distributed throughout thegarment) to determine a local surface shape of the surface of a portionof the body part covered by respective local surface areas, in block604. This local surface shape information represents a model for eachlocal are of the body part that is covered by one or more sensors and acorresponding distributed processors. The determination of this localsurface shape information may be based on geometric computation orspatial computing approach, as described above.

In block 606, local surface shape information is exchanged withneighboring processors. In some embodiments, all of a large portion ofthe distributed processors have the local surface shape information forall or most of the sensors. In block 608, a single overall model of asurface shape of the entire surface of the body part that is covered bythe garment is generated from the distributed local surface shapeinformation. In some embodiments, any one of the distributed processorsis capable of generating this single overall model based on thelocalized body state information that it receives from the otherdistributed processors. The generation of this single overall model mayalso be based on geometric computation or spatial computing approach, asdescribed above.

Optionally, in block 610, the overall model is used to generate bodykinematics information. However, the overall model information may beused in other applications such as generating measures of asymmetry tolook for Parkinson's and to provide a distributed measurement of theactivity in the motor cortex of the brain. In some embodiments, theinvention takes (records) snap shots of the overall model at differenttimes and compares the snap shots to determine the overlapped areas andthe areas that have changed from one snap shot to the next snap shots.Based on the comparison results, the invention then generates bodykinematics information such as pose, motion and muscle activation, usingknown image reconstruction techniques. In some embodiments, the overallmodel includes information about rate of change of the surface shapeover time, and the invention generates body kinematics information usingthe rate of change of the surface shape information included in themodel. Other known techniques may be used to generate the bodykinematics information from the overall model.

The generation of the body kinematics information may be performedwithin the garment by any one or the distributed processor (since everyprocessor has all the information it needs); in a central processorwithin the garment; or by one or more devices external to the garment.In some embodiments, each of the distributed processors is configured toperform adaptive processing and change the degrees of freedom beingmeasured by the plurality of sensors, according to the time of day, thetemperature, and the health of the subject wearing the garment.

Optionally, in block 612, the body kinematics information is outputted,for example, to a controller, to control an actuator. In someembodiments, at least one of the distributed processors is configured toadaptively control said actuator and respond to feedback provided by theplurality of sensors, sensor nodes and/or data streams coming fromexternal devices.

The following describes an exemplary detail technique for estimatingpose from strain data. First, locations for sensors on a nominal bodymodel are presumed for a specific joint. This way, the three dimensionallocations of sensors on body define an N×3 matrix P. Strain sensorsprovide an estimate of distance between presumed sensor locations on anominal body mapping X, which is an N×N sparse matrix of estimateddistances. Sensors provide a measurement of that matrix, that is, Z, anN×N sparse matrix of measured differences.

From these presumed prior locations and measured distances, errorcorrections are computed as:

d=Z−x

The model then converts the corrections to produce an N×N×3 perturbationtensor S. This tensor S is then applied along presumed arcs to createperturbations back to the position matrix P, which is updated to correctfor the measured errors. This process is iterated until the solutionbecomes stable. In some embodiments, this process is executed onlyaround joints, with the complete body pose being then derived throughapplication to a standard body model of joints and bones. In someembodiments, the process may be applied to entire limbs or body regionsas a means of detecting fracture or other damage to the body.

As body pose evolves over time, lines of change and movement areexamined and compared against a body model to ascertain sensor placementwith respect to specific body structures. In this way the positionmatrix P can be updated.

It will be recognized by those skilled in the art that variousmodifications may be made to the illustrated and other embodiments ofthe invention described above, without departing from the broadinventive scope thereof. It will be understood therefore that theinvention is not limited to the particular embodiments or arrangementsdisclosed, but is rather intended to cover any changes, adaptations ormodifications which are within the scope and spirit of the invention asdefined by the appended claims.

What is claimed is:
 1. A body garment for detecting body kinematicscomprising: a plurality of sensors distributed throughout the garment,each sensor being configured to sense body state information from alocal surface area of a body part covered by the garment; and aplurality of sensor nodes in proximity to the plurality of sensorsrespectively, each sensor node including a processor and configured toreceive sensing body state information from at least one of theplurality of sensors; wherein each processor is configured to receivebody state information locally from one or more sensors in proximity tosaid each processor; to utilize the information to determine a localsurface shape of the surface of a portion of the body part covered bysaid one or more sensors; and to exchange local surface shapeinformation with neighboring sensor nodes, and wherein at least oneprocessor of at least one sensor node utilizes the local surface shapeinformation received from the sensor nodes to generate one overall modelof a surface shape of the entire surface of the body part covered by thegarment.
 2. The body garment of claim 1, wherein said at least oneprocessor of at least one sensor node is further configured to generatebody kinematics information from said overall model.
 3. The body garmentof claim 2, wherein at least one of the plurality of the processors isfurther configured to output said body kinematics information to controlan actuator.
 4. The body garment of claim 3, wherein said at least oneof the plurality of the processors is further configured to adaptivelycontrol said actuator and respond to feedback provided by the pluralityof sensors, sensor nodes or data streams coming from external devices.5. The body garment of claim 2, wherein said at least one processor isconfigured to generate body kinematics information by taking snap shotsof overall model at different times and compare the snap shots.
 6. Thebody garment of claim 2, wherein said overall model includes a rate ofchange of the surface shape over time, and wherein said at least oneprocessor is configured to generate body kinematics information usingsaid rate of change of the surface shape.
 7. The body garment of claim2, wherein said body kinematics information include one or more of bodypose, gait, and muscle activation of the subject wearing the garment. 8.The body garment of claim 1, wherein said body state information includeone or more of skin surface temperature, electrical nerve impulses, andwhether a person has been at rest or is warmed up.
 9. The body garmentof claim 2, wherein at least one of the plurality of the processorsincludes an interface for driving one or more of an actuator formechanical assisting for walking, running or integration of a prostheticlimb, a haptic feedback device for delivering injury warnings, abiomechanical system for analyzing metabolic activity or athletic loadendured by a subject wearing the garment, and a health monitoringsystem.
 10. The body garment of claim 1, wherein the body garmentcomprises of a plurality of different portions, each portion configuredto be coupled to and decoupled from the other portions.
 11. The bodygarment of claim 1, wherein a first subset of the plurality of sensornodes located on critical regions of the body part is configured tooperate faster and denser than a second subset of the plurality of thesensor nodes located on stable regions of the body part.
 12. The bodygarment of claim 1, wherein the plurality of sensors are configured in aplurality of manifolds, each manifold of group of sensors including aset of similar and related sensors in close proximity of each other thatrespond in an ordered distribution, wherein the body garment is capableof losing some fraction of sensors or displacement of some of thesensors on the garment and still generate said body state information.13. The body garment of claim 1, wherein each processor is configured toperform adaptive processing to tune the local surface shape to exchangethe tuned local surface shape information with neighboring sensor nodesto commend a plurality of actuators.
 14. The body garment of claim 1,wherein each processor is configured to change degrees of freedom beingmeasured by the garment according to the time of day, the temperature,and the health of the subject wearing the garment.
 15. The body garmentof claim 1, wherein said each processor is further configured todetermine the local surface shape of the surface of a portion of thebody part covered by said one or more sensors using geometricprocessing.
 16. A method for detecting body kinematics comprising:collecting body state information from a plurality of local surfaceareas of a body part covered by a garment, from a plurality of sensorsdistributed throughout the garment covering the body part; locally andin real time processing the collected body state information of localsurface areas to determine a local surface shape of the surface of aportion of the body part covered by respective local surface areas, by arespective one of a plurality of distributed processors; exchanginglocal surface shape information with neighboring processors; andgenerating one overall model of a surface shape of the entire surface ofthe body part covered by the garment from the distributed local surfaceshape information.
 17. The method of claim 16, further comprisinggenerating body kinematics information from said overall model.
 18. Themethod of claim 17, further comprising receiving feedback from theplurality of distributed processors or from external devices; and usingsaid feedback and said body kinematics information to adaptively controlan actuator.
 19. The method of claim 16, wherein said overall modelincludes a rate of change of the surface shape over time, and furthercomprising generating body kinematics information using said rate ofchange of the surface shape.
 20. The method of claim 16, furthercomprising changing degrees of freedom being measured by the pluralityof processors according to the time of day, the temperature, and thehealth of the subject wearing the garment.